import argparse
import datetime


def 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    {
      "0": "Sunday",
      "1": "Monday",
      "2": "Tuesday",
      "3": "Wednesday",
      "4": "Thursday",
      "5": "Friday",
      "6": "Saturday",
  }

  _UpperCamelCase           =    {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

  # Validate
  if not 0 < len(a__ ) < 11:
    raise ValueError("Must be 10 characters long" )

  # Get month
  _UpperCamelCase           =    int(date_input[0] + date_input[1] )
  # Validate
  if not 0 < m < 13:
    raise ValueError("Month must be between 1 - 12" )

  _UpperCamelCase           =    date_input[2]
  # Validate
  if sep_a not in ["-", "/"]:
    raise ValueError("Date separator must be '-' or '/'" )

  # Get day
  _UpperCamelCase           =    int(date_input[3] + date_input[4] )
  # Validate
  if not 0 < d < 32:
    raise ValueError("Date must be between 1 - 31" )

  # Get second separator
  _UpperCamelCase           =    date_input[5]
  # Validate
  if sep_a not in ["-", "/"]:
    raise ValueError("Date separator must be '-' or '/'" )

  # Get year
  _UpperCamelCase           =    int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
  # Arbitrary year range
  if not 45 < y < 8_500:
    raise ValueError(
        "Year out of range. There has to be some sort of limit...right?" )

  # Get datetime obj for validation
  _UpperCamelCase           =    datetime.date(int(a__ )    ,     int(a__ )    ,     int(a__ ) )

  # Start math
  if m <= 2:
    _UpperCamelCase           =    y - 1
    _UpperCamelCase           =    m + 12
  # maths var
  _UpperCamelCase           =    int(str(a__ )[:2] )
  _UpperCamelCase           =    int(str(a__ )[2:] )
  _UpperCamelCase           =    int(2.6 * m - 5.39 )
  _UpperCamelCase           =    int(c / 4 )
  _UpperCamelCase           =    int(k / 4 )
  _UpperCamelCase           =    int(d + k )
  _UpperCamelCase           =    int(t + u + v + x )
  _UpperCamelCase           =    int(z - (2 * c) )
  _UpperCamelCase           =    round(w % 7 )
  # End math

  # Validate math
  if f != convert_datetime_days[dt_ck.weekday()]:
    raise AssertionError("The date was evaluated incorrectly. Contact developer." )

  # Response
  _UpperCamelCase           =    f'Your date {date_input}, is a {days[str(a__ )]}!'
  return response


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   lowerCamelCase__  =					argparse.ArgumentParser(
       description=(
           '''Find out what day of the week nearly any date is or was. Enter '''
           '''date as a string in the mm-dd-yyyy or mm/dd/yyyy format'''
       )
   )
   parser.add_argument(
       '''date_input''', type=str, help='''Date as a string (mm-dd-yyyy or mm/dd/yyyy)'''
   )
   lowerCamelCase__  =					parser.parse_args()
   zeller(args.date_input)





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    fname.split(os.path.sep )[-1]
  return re.search(r"^(.*)_\d+\.jpg$"    ,     a__ ).groups()[0]




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   int							, lowercase_				:   Dict=None							, lowercase_				:   Optional[Any]=None)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    file_names
   _UpperCamelCase           =    image_transform
   _UpperCamelCase           =    label_to_id







 def __len__(		self				:   int)						->	Optional[int]:


   """simple docstring"""







   return len(self.file_names)







 def __getitem__(		self				:   int							, lowercase_				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    self.file_names[idx]
   _UpperCamelCase           =    PIL.Image.open(lowercase_)
   _UpperCamelCase           =    raw_image.convert("RGB")
   if self.image_transform is not None:
     _UpperCamelCase           =    self.image_transform(lowercase_)
   _UpperCamelCase           =    extract_label(lowercase_)
   if self.label_to_id is not None:
     _UpperCamelCase           =    self.label_to_id[label]
   return {"image": image, "label": label}


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if args.with_tracking:
    _UpperCamelCase           =    Accelerator(
        cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     log_with="all"    ,     project_dir=args.project_dir )
  else:
    _UpperCamelCase           =    Accelerator(cpu=args.cpu    ,     mixed_precision=args.mixed_precision )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCamelCase           =    config["lr"]
  _UpperCamelCase           =    int(config["num_epochs"] )
  _UpperCamelCase           =    int(config["seed"] )
  _UpperCamelCase           =    int(config["batch_size"] )
  _UpperCamelCase           =    config["image_size"]
  if not isinstance(a__    ,     (list, tuple) ):
    _UpperCamelCase           =    (image_size, image_size)

  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps    ,     "isdigit" ):
    if args.checkpointing_steps == "epoch":
      _UpperCamelCase           =    args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _UpperCamelCase           =    int(args.checkpointing_steps )
    else:
      raise ValueError(
          f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
  else:
    _UpperCamelCase           =    None

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    _UpperCamelCase           =    os.path.split(a__ )[-1].split("." )[0]
    accelerator.init_trackers(a__    ,     a__ )

  # Grab all the image filenames
  _UpperCamelCase           =    [os.path.join(args.data_dir    ,     a__ ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]

  # Build the label correspondences
  _UpperCamelCase           =    [extract_label(a__ ) for fname in file_names]
  _UpperCamelCase           =    list(set(a__ ) )
  id_to_label.sort()
  _UpperCamelCase           =    {lbl: i for i, lbl in enumerate(a__ )}

  # Set the seed before splitting the data.
  np.random.seed(a__ )
  torch.manual_seed(a__ )
  torch.cuda.manual_seed_all(a__ )

  # Split our filenames between train and validation
  _UpperCamelCase           =    np.random.permutation(len(a__ ) )
  _UpperCamelCase           =    int(0.8 * len(a__ ) )
  _UpperCamelCase           =    random_perm[:cut]
  _UpperCamelCase           =    random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  _UpperCamelCase           =    Compose([RandomResizedCrop(a__    ,     scale=(0.5, 1.0) ), ToTensor()] )
  _UpperCamelCase           =    PetsDataset(
      [file_names[i] for i in train_split]    ,     image_transform=a__    ,     label_to_id=a__ )

  # For evaluation, we use a deterministic Resize
  _UpperCamelCase           =    Compose([Resize(a__ ), ToTensor()] )
  _UpperCamelCase           =    PetsDataset([file_names[i] for i in eval_split]    ,     image_transform=a__    ,     label_to_id=a__ )

  # Instantiate dataloaders.
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCamelCase           =    create_model("resnet50d"    ,     pretrained=a__    ,     num_classes=len(a__ ) )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _UpperCamelCase           =    model.to(accelerator.device )

  # Freezing the base model
  for param in model.parameters():
    _UpperCamelCase           =    False
  for param in model.get_classifier().parameters():
    _UpperCamelCase           =    True

  # We normalize the batches of images to be a bit faster.
  _UpperCamelCase           =    torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
  _UpperCamelCase           =    torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )

  # Instantiate optimizer
  _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()    ,     lr=lr / 25 )

  # Instantiate learning rate scheduler
  _UpperCamelCase           =    OneCycleLR(optimizer=a__    ,     max_lr=a__    ,     epochs=a__    ,     steps_per_epoch=len(a__ ) )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
      a__    ,     a__    ,     a__    ,     a__    ,     a__ )
  # We need to keep track of how many total steps we have iterated over
  _UpperCamelCase           =    0
  # We also need to keep track of the starting epoch so files are named properly
  _UpperCamelCase           =    0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}' )
      accelerator.load_state(args.resume_from_checkpoint )
      _UpperCamelCase           =    os.path.basename(args.resume_from_checkpoint )
    else:
      # Get the most recent checkpoint
      _UpperCamelCase           =    [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
      dirs.sort(key=os.path.getctime )
      _UpperCamelCase           =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _UpperCamelCase           =    os.path.splitext(a__ )[0]

    if "epoch" in training_difference:
      _UpperCamelCase           =    int(training_difference.replace("epoch_"    ,     "" ) ) + 1
      _UpperCamelCase           =    None
    else:
      _UpperCamelCase           =    int(training_difference.replace("step_"    ,     "" ) )
      _UpperCamelCase           =    resume_step // len(a__ )
      resume_step -= starting_epoch * len(a__ )

    # Now we train the model
  for epoch in range(a__    ,     a__ ):
    model.train()
    if args.with_tracking:
      _UpperCamelCase           =    0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      _UpperCamelCase           =    accelerator.skip_first_batches(a__    ,     a__ )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _UpperCamelCase           =    train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase           =    {k: v.to(accelerator.device ) for k, v in batch.items()}
      _UpperCamelCase           =    (batch["image"] - mean) / std
      _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    torch.nn.functional.cross_entropy(a__    ,     batch["label"] )
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(a__ )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      overall_step += 1
      if isinstance(a__    ,     a__ ):
        _UpperCamelCase           =    f'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _UpperCamelCase           =    os.path.join(args.output_dir    ,     a__ )
          accelerator.save_state(a__ )
    model.eval()
    _UpperCamelCase           =    0
    _UpperCamelCase           =    0
    for step, batch in enumerate(a__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase           =    {k: v.to(accelerator.device ) for k, v in batch.items()}
      _UpperCamelCase           =    (batch["image"] - mean) / std
      with torch.no_grad():
        _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    outputs.argmax(dim=-1 )
      _UpperCamelCase      ,						_UpperCamelCase           =    accelerator.gather_for_metrics((predictions, batch["label"]) )
      _UpperCamelCase           =    predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    _UpperCamelCase           =    accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}' )
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": 100 * eval_metric,
              "train_loss": total_loss.item() / len(a__ ),
              "epoch": epoch,
          }    ,     step=a__    ,     )
    if checkpointing_steps == "epoch":
      _UpperCamelCase           =    f'epoch_{epoch}'
      if args.output_dir is not None:
        _UpperCamelCase           =    os.path.join(args.output_dir    ,     a__ )
      accelerator.save_state(a__ )

  if args.with_tracking:
    accelerator.end_training()


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Simple example of training script." )
  parser.add_argument("--data_dir"    ,     required=a__    ,     help="The data folder on disk." )
  parser.add_argument("--fp16"    ,     action="store_true"    ,     help="If passed, will use FP16 training." )
  parser.add_argument(
      "--mixed_precision"    ,     type=a__    ,     default=a__    ,     choices=["no", "fp16", "bf16", "fp8"]    ,     help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU."    ,     )
  parser.add_argument("--cpu"    ,     action="store_true"    ,     help="If passed, will train on the CPU." )
  parser.add_argument(
      "--checkpointing_steps"    ,     type=a__    ,     default=a__    ,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,     )
  parser.add_argument(
      "--output_dir"    ,     type=a__    ,     default="."    ,     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,     )
  parser.add_argument(
      "--resume_from_checkpoint"    ,     type=a__    ,     default=a__    ,     help="If the training should continue from a checkpoint folder."    ,     )
  parser.add_argument(
      "--with_tracking"    ,     action="store_true"    ,     help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,     )
  parser.add_argument(
      "--project_dir"    ,     type=a__    ,     default="logs"    ,     help="Location on where to store experiment tracking logs` and relevent project information"    ,     )
  _UpperCamelCase           =    parser.parse_args()
  _UpperCamelCase           =    {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  training_function(a__    ,     a__ )


if __name__ == "__main__":
   main()




